Scaled and oriented object tracking using ensemble of multilayer perceptrons

Abstract

Major challenging problems in the field of moving object tracking are to handle changing in scale and orientation, background clutter and large variation in pose with occlusion. This article presents an algorithm to track moving object under such complex environment. Here, a discriminative model based on an ensemble of multilayer perceptrons (MLPs) is proposed to detect object from its cluttered background. Orientation and enhanced scale of the detected object is estimated using binary moments. Here, the problem of object tracking is posed as a constrained optimization with respect to location, scale and orientation of the object. Two different heuristics based on support value and confidence score are proposed to reduce drift and to detect full occlusion. Three benchmark datasets are considered for the experimental purpose and the proposed algorithm attains state-of-the-art performance under various conditions.